Automotive Science and Engineering
Iran University of Science & Technology
Mon, Jul 15, 2024
[Archive]
Volume 5, Issue 1 (3-2015)
ASE 2015, 5(1): 923-931 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Kazemi R, Abdollahzade M. A Solution to the Problem of Extrapolation in Car Following Modeling Using an online fuzzy Neural Network. ASE 2015; 5 (1) :923-931
URL: http://www.iust.ac.ir/ijae/article-1-300-en.html
URL: http://www.iust.ac.ir/ijae/article-1-300-en.html
Abstract: (18691 Views)
Car following process is time-varying in essence, due to the involvement of human actions. This paper develops an adaptive technique for car following modeling in a traffic flow. The proposed technique includes an online fuzzy neural network (OFNN) which is able to adapt its rule-consequent parameters to the time-varying processes. The proposed OFNN is first trained by an growing binary tree learning algorithm in offline mode, which produces favorable extrapolation performance, and then, is adapted to the stream of car following data, e.g. velocity and acceleration of the target vehicle, using an adaptive least squares estimation. The proposed approach is validated by means of real-world car following data sets. Simulation results confirm the satisfactory performance of the OFNN for adaptive car following modeling application.
Type of Study: Research |
Subject:
Internal Combustion Engines (ICE, ...)
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
